Devices used by automated milking systems are similarly accurate in estimating milk yield and in collecting a representative milk sample compared with devices used by farms with conventional milk recording

Kamphuis, C.; Rue, B. Dela; Turner, Sally-Anne; Petch, S. F.

doi:10.3168/jds.2014-8714

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Milk samples were taken in the morning milking session during milk letdown using removable sampling bottles mounted into the milking line after the outlet port of the ICAR-approved milk meter (ICAR, 2017). Such a milk sampling device enables collection of representative milk samples of sufficient quantity (i.e., >20 cm 3 ) from all stages of the milking session for the determination of chemical composition (Kamphuis et al, 2015). Upon completion of milking, each sample was transferred from the collection bottle to a 60-cm 3 sample container.…”

Section: Sample Collection and Laboratory Analysismentioning

confidence: 99%

Effect of genetic and nongenetic factors on chemical composition of individual milk samples from dromedary camels (Camelus dromedarius) under intensive management

Nagy

Fábri

Varga

et al. 2017

Journal of Dairy Science

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The aims of the present study were to monitor the changes in gross chemical composition of individual dromedary camel milk over a 5-yr period, to provide reference values, and to determine the effect of genetic and nongenetic factors influencing camel milk composition under intensive management. A total of 1,528 lactating dromedary camels were included in the study. Animals were fed a constant diet and were milked twice a day in a herringbone parlor. Milk samples were collected at monthly intervals using a sampling device and then fat, protein, lactose, total solids (TS), and solids-nonfat (SNF) concentrations of raw camel milk were determined with an automatic milk analyzer. For each milk sample, production parameters were recorded and quantities (grams) of milk constituents were calculated. The overall mean quantity and fat, protein, lactose, SNF, and TS concentrations of the morning milk were 4.0 kg, 2.58%, 2.95%, 4.19%, 8.08%, and 10.46%, respectively. Milk quantity showed a positive correlation with lactose and a negative correlation with all other components. Parity exerted a strong effect on all milk parameters. Primiparous dromedaries (n = 60) produced less milk with higher concentrations of components than did multiparous animals (n = 1,468). Milk composition varied among the 7 breeds tested, but none of the genotypes was found to be superior to the others in this respect. We detected a significant, yet small calf sex-biased difference in milk yield and composition. Stage of lactation and season strongly influenced milk yield and all milk components. We also found a significant interaction between month postpartum (mPP) and month of the year. The concentration of all milk components decreased from 1 to 5 mPP. Later, lactose concentration and quantity continued to decrease parallel with decreasing milk production. The concentration of other components showed a temporary increase in mid lactation, from 6 to 11 mPP, and in late lactation, from 18 to 23 mPP. Mean fat, protein, SNF, and TS concentrations showed a high seasonal variation (9.5 to 28.7%), with the lowest and highest values being measured during summer and winter, respectively. This seasonal variation was independent of nutrition and may reflect an endogenous circannual rhythm. We observed a noticeable variation among years. Dromedary camels could provide a useful in vivo model to study the homeorhetic regulation of mammary cell function by endogenous and environmental factors.

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Section: Sample Collection and Laboratory Analysismentioning

confidence: 99%

Effect of genetic and nongenetic factors on chemical composition of individual milk samples from dromedary camels (Camelus dromedarius) under intensive management

Nagy

Fábri

Varga

et al. 2017

Journal of Dairy Science

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“…For comparison, the International Committee for Animal Recording (ICAR) recording guidelines state that automatic milking systems should measure the milk yield with a standard deviation of no more than 5% of the reference milk yield for milkings where more than 10 kg are produced (ICAR, 2016). The observation that the accuracy of the sensor systems in the milking robots does not live up to the ICAR standards is supported by Kamphuis et al (2015). Given this knowledge, it seems likely that the observational variance seen in our study is largely due to measurement errors on the farms.…”

Section: Discussionmentioning

confidence: 55%

Dynamic forecasting of individual cow milk yield in automatic milking systems

Jensen

Voort

Hogeveen

2018

Journal of Dairy Science

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Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.

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“…A detailed description of the data collection process has been described previously (Kamphuis et al 2015). Briefly, data were collected on five farms with AMS located across NZ between December 2011 and February 2013.…”

Section: Methodsmentioning

confidence: 99%

Shorter sampling periods and accurate estimates of milk volume and components are possible for pasture based dairy herds milked with automated milking systems

Kamphuis

Burke

Taukiri

et al. 2016

Journal of Dairy Research

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Dairy cows grazing pasture and milked using automated milking systems (AMS) have lower milking frequencies than indoor fed cows milked using AMS. Therefore, milk recording intervals used for herd testing indoor fed cows may not be suitable for cows on pasture based farms. We hypothesised that accurate standardised 24 h estimates could be determined for AMS herds with milk recording intervals of less than the Gold Standard (48 hs), but that the optimum milk recording interval would depend on the herd average for milking frequency. The Gold Standard protocol was applied on five commercial dairy farms with AMS, between December 2011 and February 2013. From 12 milk recording test periods, involving 2211 cow-test days and 8049 cow milkings, standardised 24 h estimates for milk volume and milk composition were calculated for the Gold Standard protocol and compared with those collected during nine alternative sampling scenarios, including six shorter sampling periods and three in which a fixed number of milk samples per cow were collected. Results infer a 48 h milk recording protocol is unnecessarily long for collecting accurate estimates during milk recording on pasture based AMS farms. Collection of two milk samples only per cow was optimal in terms of high concordance correlation coefficients for milk volume and components and a low proportion of missed cow-test days. Further research is required to determine the effects of diurnal variations in milk composition on standardised 24 h estimates for milk volume and components, before a protocol based on a fixed number of samples could be considered. Based on the results of this study New Zealand have adopted a split protocol for herd testing based on the average milking frequency for the herd (NZ Herd Test Standard 8100:2015).

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Devices used by automated milking systems are similarly accurate in estimating milk yield and in collecting a representative milk sample compared with devices used by farms with conventional milk recording

Cited by 7 publications

References 9 publications

Effect of genetic and nongenetic factors on chemical composition of individual milk samples from dromedary camels (Camelus dromedarius) under intensive management

Effect of genetic and nongenetic factors on chemical composition of individual milk samples from dromedary camels (Camelus dromedarius) under intensive management

Dynamic forecasting of individual cow milk yield in automatic milking systems

Shorter sampling periods and accurate estimates of milk volume and components are possible for pasture based dairy herds milked with automated milking systems

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